Comparative analysis of RNN versus IIR digital filtering to optimize resilience to dynamic perturbations in pH sensing for vertical farming

Vertical Farming (VF) refers to systems of agriculture where crops are
grown in trays stacked vertically by exposing them to artificial light and
using sensing technology to improve product quality and yield. In this
work, we propose an advanced filtering scheme based on Recurrent Neural
Networks (RNNs) and Deep Learning to enable efficient control strategies
for VF applications.  We demonstrate that the best RNN model
incorporates five neuron layers, with the first and second containing
ninety Long Short-Term Memory neurons.  The third layer
implements one Gated Recurrent Units neuron.  The fourth segment
incorporates one RNN network, while the output layer is designed by using
a single neuron exhibiting a rectified linear activation
function.  By utilizing this RNN digital filter, we introduce two
variations: (1) A scaled RNN model to tune the filter to the signal of
interest, and (2) A moving average filter to eliminate harmonic
oscillations of the output waveforms. The RNN models are contrasted with
conventional digital Butterworth, Chebyshev I, Chebyshev II, and Elliptic
Infinite Impulse Response (IIR) configurations.  The RNN digital
filtering schemes avoid introducing unwanted oscillations, which makes
them more suitable for VF than their IIR counterparts.  Finally,
by utilizing the advanced features of scaling of the RNN model, we
demonstrate that the RNN digital filter can be pH selective, as opposed to
conventional IIR filters.